Abstract

There has been a recent surge in the implementation of electronic health care records. These patient records contain valuable medical information including patient demographic data, diagnosis, therapeutic approach, and patient outcomes. It is important to analyze patterns within these records in order to more effectively treat individuals. In this paper, a method is presented for identifying these themes and patterns within patient data. This methodology includes extraction of the main themes or patterns in the data and linking those themes back to the corpus from which they were generated. In our research, we partitioned graphs from terms gathered from electronic medical records. We used two sets of data including eight charts and ten case studies for this study from primary disease categories. The Electronic Medical Records (EMRs) and case studies were modeled as networks of interacting terms where the interactions were captured by their co-occurrences in the documents. A greedy algorithm was used to find communities with high modularity. Finally, we compared our method with probabilistic topic modeling algorithms and evaluated the efficacy of our method by using recall and precision measures.